Intel Xeon CPU
Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for MXNet.
GPU Dedicated Server for MXNet and Deep Learning
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It is portable and lightweight, scalable to many GPUs and machines. We provide bare metal servers with GPUs that are specifically designed for deep learning with MXNet.
Plans & Prices of GPU Servers for MXNet
We offer cost-effective and optimized NVIDIA GPU servers for MXNet
Spring Sale
Basic GPU - K80
$ 87.20/mo
Save 20% (Was $109.00)
1m3m12m24m
Order Now- 64GB RAM
- Eight-Core Xeon E5-2690report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Tesla K80
- Microarchitecture: Turing
- Max GPUs: 2report
- CUDA Cores: 4992
- GPU Memory: 24GB GDDR5
- FP32 Performance: 8.73 TFLOPSreport
Basic GPU - RTX 4060
$ 149.00/mo
1m3m12m24m
Order Now- 64GB RAM
- Eight-Core E5-2690report
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia GeForece RTX 4060
- Microarchitecture: Ada Lovelace
- Max GPUs: 2report
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPSreport
Advanced GPU - A4000
$ 209.00/mo
1m3m12m24m
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A4000
- Microarchitecture: Ampere
- Max GPUs: 2report
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPSreport
Advanced GPU - V100
$ 229.00/mo
1m3m12m24m
Order Now- 128GB RAM
- Dual 12-Core E5-2690v3report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- Max GPUs: 1
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPSreport
Advanced GPU - A5000
$ 269.00/mo
1m3m12m24m
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- Max GPUs: 2report
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPSreport
Enterprise GPU - RTX 4090
$ 409.00/mo
1m3m12m24m
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4report
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- Max GPUs: 1
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPSreport
Enterprise GPU - A40
$ 439.00/mo
1m3m12m24m
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4report
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia A40
- Microarchitecture: Ampere
- Max GPUs: 1
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPSreport
Enterprise GPU - RTX A6000
$ 409.00/mo
1m3m12m24m
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4report
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- Max GPUs: 1
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPSreport
More GPU Hosting Plansarrow_circle_right
6 Reasons to Choose our GPU Servers for MXNet
DBM enables powerful MXNet GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
SSD-Based Drives
You can never go wrong with our own top-notch GPU dedicated servers, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your GPU dedicated server very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for MXNet and networks.
Dedicated IP
One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.
DDoS Protection
Resources among different users are fully isolated to ensure your data security. DBM protects against DDoS from the edge fast while ensuring legitimate traffic of hosted GPUs for MXNet is not compromised.
Advantages of Deep Learning with MXNet
Here are some of the areas in which MXNet compares favorably to existing alternatives.
User-Friendly and Easy-to-Use
MXNet has the NumPy-like programming interface and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start deep learning projects.
Hybrid Front-End
Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
Rich Ecosystem
Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects, such as TVM, TensorRT, OpenVINO.
Distributed Training
Scales up to multi-GPUs-and-distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
Efficiency and Flexibility
Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
10+ Language Bindings
Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, Julia , etc.
Feature Comparison: MXNet vs Keras vs PyTorch vs TensorFlow
Everyone's situation and needs are different, so it boils down to which features matter the most for your AI projects.
| Features | MXNet | Keras | PyTorch | TensorFlow |
|---|---|---|---|---|
| API Level | High and low | High | Low | High and low |
| Architecture | Complex, less readable | Simple, concise, readable | Complex, less readable | Not easy to use |
| Datasets | Large datasets, high performance | Smaller datasets | Large datasets, high performance | Large datasets, high performance |
| Debugging | Hard to debug pure symbol codes | Simple network, so debugging is not often needed | Good debugging capabilities | Difficult to conduct debugging |
| Trained Models Included | Yes | Yes | Yes | Yes |
| Popularity | Fourth most popular | Most popular | Third most popular | Second most popular |
| Speed | Fastest on ResNet-50, high performance | Slow, low performance | Fastest on Faster-RCNN, high performance | Fastest on VGG-16, high performance |
| Written In | C++, Python | Python | Lua, LuaJIT, C, CUDA, and C++ | C++, CUDA, Python |
FAQs of GPUs for MXNet
A list of frequently asked questions about GPU rental servers for MXNet.
What is MXNet used for?
MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It's highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. To get started Deep learning with MXNet, you often need GPUs for MXNet.
Who uses MXNet?
MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS.
What is the relationship between MXNet and TensorFlow?
Both MXNet and TensorFlow use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a mixed approach, with a dynamic dependency scheduler to combine symbolic and imperative programming.
What are the best GPUs for MXNet deep learning?
Today, leading vendor NVIDIA offers the best GPUs for MXNet deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for MXNet in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for MXNet.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for MXNet.
How can I run a MXNet model on multiple GPUs?
To do the multiple MXNet GPUs training, you need to initialize a model on all GPUs, split the batches of data into separate splits where each is stored on a different GPU and run the model separately on every split. The synchronization of gradients and parameters between GPUs is done automatically by Apache MXNet.
Is MXNet faster than PyTorch?
In Apache MXNet, you don't need to flatten the 4-D input into 2-D when feeding the data into forward pass. MXNet has the fastest training speed on ResNet-50, and PyTorch is the fastest on Faster-RCNN. Though you need to be cautious with such toy comparisons.
Is MXNet better than TensorFlow?
Compared to TensorFlow, MXNet has a smaller open-source community. Improvements, bug fixes, and other features take longer due to a lack of major community support. Despite being widely used by many organizations in the tech industry, MXNet is not as popular as Tensorflow.
What is the advantage of bare metal GPUs for MXNet?
Bare metal GPU servers for MXNet will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
DBM GPU Servers for MXNet are all bare metal servers, so we have best GPU dedicated server for AI.
DBM GPU Servers for MXNet are all bare metal servers, so we have best GPU dedicated server for AI.
Get Started Resources
Get what you need to get started with MXNet quickly.
Prerequisites
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1. Choose a plan and place an order
2. Ubuntu 16.04 or higher, Windows 10 or higher
3. Install NVIDIA® CUDA® Toolkit & cuDNN
4. Python 3.6 - 3.8 recommended
Step-by-Step Instructions
Go to MXNet's site, read the installation guide.
1. Indicate your preferred configuration to see specific instructions.
Sample:
2. Install MXNet with pip
Make sure your installed CUDA version matches the CUDA version in the pip package.
Sample: pip install --upgrade pip pip install mxnet-cu112
Make sure your installed CUDA version matches the CUDA version in the pip package.
Sample: # Python with GPU, use mx.gpu(), to set MXNet context to be GPUs import mxnet as mx a = mx.nd.ones((2, 3), mx.gpu()) b = a * 2 + 1 b.asnumpy() # If you don't get an import error, then MXNet is ready for python.